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arXiv · 1905.08537

Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search

Youhei Akimoto·Shinichi Shirakawa·Nozomu Yoshinari·Kento Uchida·Shota Saito·Kouhei Nishida
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Abstract

High sensitivity of neural architecture search (NAS) methods against their input such as step-size (i.e., learning rate) and search space prevents practitioners from applying them out-of-the-box to their own problems, albeit its purpose is to automate a part of tuning process. Aiming at a fast, robust, and widely-applicable NAS, we develop a generic optimization framework for NAS. We turn a coupled optimization of connection weights and neural architecture into a differentiable optimization by means of stochastic relaxation. It accepts arbitrary search space (widely-applicable) and enables to employ a gradient-based simultaneous optimization of weights and architecture (fast). We propose a stochastic natural gradient method with an adaptive step-size mechanism built upon our theoretical investigation (robust). Despite its simplicity and no problem-dependent parameter tuning, our method exhibited near state-of-the-art performances with low computational budgets both on image classification and inpainting tasks.

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Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, Kouhei Nishida. 2019-05-21. Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search. https://arxiv.org/abs/1905.08537

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